{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save and Load Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving and loading trained Deep Learning models has multiple valuable uses. These models are often costly to train; storing a pre-trained model can help reduce costs as it can be loaded and reused to forecast multiple times. Moreover, it enables Transfer learning capabilities, consisting of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications.\n",
    "\n",
    "In this notebook we show an example on how to save and load `NeuralForecast` models.\n",
    "\n",
    "The two methods to consider are:<br>\n",
    "1. `NeuralForecast.save`: Saves models into disk, allows save dataset and config.<br>\n",
    "2. `NeuralForecast.load`: Loads models from a given path.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "This Guide assumes basic knowledge on the NeuralForecast library. For a minimal example visit the [Getting Started](./Getting_Started.ipynb) guide.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Save_Load_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading AirPassengers Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will use the classical [AirPassenger Data set](https://www.kaggle.com/datasets/rakannimer/air-passengers). Import the pre-processed AirPassenger from `utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cchallu/opt/anaconda3/envs/neuralforecast/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from neuralforecast.utils import AirPassengersDF\n",
    "\n",
    "Y_df = AirPassengersDF\n",
    "Y_df = Y_df.reset_index(drop=True)\n",
    "Y_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we instantiate and train three models: `NBEATS`, `NHITS`, and `AutoMLP`. The models with their hyperparameters are defined in the `models` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.auto import AutoMLP\n",
    "from neuralforecast.models import NBEATS, NHITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "horizon = 12\n",
    "models = [NBEATS(input_size=2 * horizon, h=horizon, max_steps=50),\n",
    "          NHITS(input_size=2 * horizon, h=horizon, max_steps=50),\n",
    "          AutoMLP(# Ray tune explore config\n",
    "                  config=dict(max_steps=100, # Operates with steps not epochs\n",
    "                              input_size=tune.choice([3*horizon]),\n",
    "                              learning_rate=tune.choice([1e-3])),\n",
    "                  h=horizon,\n",
    "                  num_samples=1, cpus=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=models, freq='M')\n",
    "nf.fit(df=Y_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Produce the forecasts with the `predict` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 98.79it/s] \n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 123.41it/s]\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 161.79it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>NBEATS</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>AutoMLP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>428.410553</td>\n",
       "      <td>445.268158</td>\n",
       "      <td>452.550446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>425.958557</td>\n",
       "      <td>469.293945</td>\n",
       "      <td>442.683807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>477.748016</td>\n",
       "      <td>462.920807</td>\n",
       "      <td>474.043457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>477.548798</td>\n",
       "      <td>489.986633</td>\n",
       "      <td>503.836334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>495.973541</td>\n",
       "      <td>518.612610</td>\n",
       "      <td>531.347900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      NBEATS       NHITS     AutoMLP\n",
       "0        1.0 1961-01-31  428.410553  445.268158  452.550446\n",
       "1        1.0 1961-02-28  425.958557  469.293945  442.683807\n",
       "2        1.0 1961-03-31  477.748016  462.920807  474.043457\n",
       "3        1.0 1961-04-30  477.548798  489.986633  503.836334\n",
       "4        1.0 1961-05-31  495.973541  518.612610  531.347900"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict().reset_index()\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the forecasts for each model. Note how the two `NBEATS` models are differentiated with a numerical suffix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plt.figure(figsize = (12, 3))\n",
    "plot_df[['y', 'NBEATS', 'NHITS', 'AutoMLP']].plot(linewidth=2)\n",
    "\n",
    "plt.title('AirPassengers Forecast', fontsize=10)\n",
    "plt.ylabel('Monthly Passengers', fontsize=10)\n",
    "plt.xlabel('Timestamp [t]', fontsize=10)\n",
    "plt.axvline(x=plot_df.index[-horizon], color='k', linestyle='--', linewidth=2)\n",
    "plt.legend(prop={'size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Save models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save all the trained models use the `save` method. This method will save both the hyperparameters and the learnable weights (parameters).\n",
    "\n",
    "The `save` method has the following inputs:\n",
    "\n",
    "* `path`: directory where models will be saved.\n",
    "* `model_index`: optional list to specify which models to save. For example, to only save the `NHITS` model use `model_index=[2]`.\n",
    "* `overwrite`: boolean to overwrite existing files in `path`. When True, the method will only overwrite models with conflicting names.\n",
    "* `save_dataset`: boolean to save `Dataset` object with the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nf.save(path='./checkpoints/test_run/',\n",
    "        model_index=None, \n",
    "        overwrite=True,\n",
    "        save_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each model, two files are created and stored:\n",
    "\n",
    "* `[model_name]_[suffix].ckpt`: Pytorch Lightning checkpoint file with the model parameters and hyperparameters.\n",
    "* `[model_name]_[suffix].pkl`: Dictionary with configuration attributes.\n",
    "\n",
    "Where `model_name` corresponds to the name of the model in lowercase (eg. `nhits`). We use a numerical suffix to distinguish multiple models of each class. In this example the names will be `automlp_0`, `nbeats_0`, and `nhits_0`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "The `Auto` models will be stored as their base model. For example, the `AutoMLP` trained above is stored as an `MLP` model, with the best hyparparameters found during tuning.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Load models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the saved models with the `load` method, specifying the `path`, and use the new `nf2` object to produce forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 153.75it/s]\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 142.04it/s]\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 105.82it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>MLP</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>NBEATS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>452.550446</td>\n",
       "      <td>445.268158</td>\n",
       "      <td>428.410553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>442.683807</td>\n",
       "      <td>469.293945</td>\n",
       "      <td>425.958557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>474.043457</td>\n",
       "      <td>462.920807</td>\n",
       "      <td>477.748016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>503.836334</td>\n",
       "      <td>489.986633</td>\n",
       "      <td>477.548798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>531.347900</td>\n",
       "      <td>518.612610</td>\n",
       "      <td>495.973541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds         MLP       NHITS      NBEATS\n",
       "0        1.0 1961-01-31  452.550446  445.268158  428.410553\n",
       "1        1.0 1961-02-28  442.683807  469.293945  425.958557\n",
       "2        1.0 1961-03-31  474.043457  462.920807  477.748016\n",
       "3        1.0 1961-04-30  503.836334  489.986633  477.548798\n",
       "4        1.0 1961-05-31  531.347900  518.612610  495.973541"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf2 = NeuralForecast.load(path='./checkpoints/test_run/')\n",
    "Y_hat_df = nf2.predict().reset_index()\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, plot the forecasts to confirm they are identical to the original forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = pd.concat([Y_df, Y_hat_df]).set_index('ds') # Concatenate the train and forecast dataframes\n",
    "\n",
    "plt.figure(figsize = (12, 3))\n",
    "plot_df[['y', 'NBEATS', 'NHITS', 'MLP']].plot(linewidth=2)\n",
    "\n",
    "plt.title('AirPassengers Forecast', fontsize=10)\n",
    "plt.ylabel('Monthly Passengers', fontsize=10)\n",
    "plt.xlabel('Timestamp [t]', fontsize=10)\n",
    "plt.axvline(x=plot_df.index[-horizon], color='k', linestyle='--', linewidth=2)\n",
    "plt.legend(prop={'size': 10})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "https://pytorch-lightning.readthedocs.io/en/stable/common/checkpointing_basic.html\n",
    "\n",
    "[Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. ICLR 2020](https://arxiv.org/abs/1905.10437)\n",
    "\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
